Load forecasting task of small energetic region is a difficult problem due to high variability of power consumption. The accurate forecast of the power in the next hours is very important from the economic point of view. The most important problems in prediction are the choice of predictor and selection of features. Two methods of features selection was presented – simple selection using of genetic algorithm and dimensionality reduction methods for creating new features from many available measured data. As a predictor the Support Vector Machine working in regression mode (SVR) was chosen. The load forecasting results with SVR are presented and discussed.


load power forecasting; dimensionality reduction; genetic algorithm; support vector machine

Ashlock D.: Evolutionary Computation for Modeling and Optimization. Berlin, Germany: Springer-Verlag, 2006.

Fodor I.: A Survey of Dimension Reduction Techniques. Raport techniczny, 2002.

Gill P., Murray W., Wright M.: Practical optimization. Academic Press, London 1981.

Goldberg D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.

Jackson J.E.: User guide to principal components. Wiley, NY, 1991.

Osowski S., Siwek K., Świderski B., Mycka Ł.: Prediction of power consumption for small power region using indexing approach and neural network. Lecture Notes on Computer Science, LNCS-6352, 2010, str. 54-59.

Osowski S., Siwek K.: Regularization of neural networks for load forecasting in power system. IEE Proc. GTD, 149, 2002, 340-345.

Sammon J.W.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, No. 18, 1969, str. 401–409.

Siwek K., Osowski S., Świderski B.: Trend elimination of time series of 24-hour load demand in the power system and its application in power forecasting. Przegląd Elektrotechniczny, vol. 87, No 3, 2011, str. 249-253.

Schölkopf B., Smola A.: Learning with kernels. MIT Press, Cambridge MA, 2002.

Vapnik V.: Statistical learning theory. Wiley, NY, 1998.

Van der Maaten L., Hinton G.: Visualizing Data using t-SNE, Journal of Machine Learning Research, Vol. 9, 2008, str. 2579-2605.

Van der Maaten L., Postma, E.: Dimensionality reduction: a comparative review. 2009, int. report TiCC TR 2009-005.

Published : 2013-05-16

Siwek, K. (2013). DATA REDUCTION VERSUS FEATURE SELECTION IN APPLICATION OF DAILY MAXIMUM POWER LOAD FORECASTING. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 3(2), 9-12.

Krzysztof Siwek
Warsaw University of Technology  Poland